“I Am Less Stressed, More Productive”: A Mixed-Methods Analysis of Stress-Management Interventions and Their Impact on Employee Well-Being and Performance at Saudi Universities

Abstract

This study investigates workplace stress-management practices and their relationships with employees’ well-being and productivity in accordance with Tayma University College’s goals in Saudi Vision 2030. Although stress-relief programs have been studied in detail in Western cultural environments, efficacy in the context of Saudi higher education institutions has proven to be limited, particularly as employee reactions are shaped by cultural, organizational, and institutional factors. This paper aims to explore the relationships between various other indicators, namely, mindfulness, time management, scheduling autonomy, and coworker support, and stress, job performance, and work–life balance. A convergent mixed-methods design was utilized, based on survey responses from 104 academic and administrative employees and semi-structured interviews with 20 respondents. The presentation of data demonstrated that time management was most consistently and significantly effective using SEM. In conclusion, time management was positively and significantly associated with increased schedule control, coworker support, and job performance, resulting in a more balanced work–life experience. Mindfulness had no significant or meaningful influence on perceived stress levels, while the influence of coworker support was more variable, and job performance experienced greater variation. Qualitative results confirmed this trend, as evidenced by the fact that time-management-oriented activities were incorporated into the daily routine, while mindfulness-related exercises were not well integrated with the cultural norms and work requirements. Within the university context of Saudi Arabia and with reference to the Job Demands–Resources (JDs–Rs) framework and the Transactional Model of Stress and Coping, the study also reveals that situational influences constitute a significant contribution to the development and use of stress-relief resources. Ultimately, the findings highlight the value of culturally relevant stress-management practices to facilitate the well-being, performance, and stability of employees with the backdrop of Saudi Vision 2030.

1. Introduction

The development of globalization and the continued acceleration of digital technologies have gradually reconfigured organizational work and raised the demand for more and higher expectations that are expected as well as roles for workers. Workforces in all areas face deadlines and navigate complex interpersonal relationships and continue to perform well over time. Such conditions have resulted in the increased prevalence of job stress, which has undeniable effects on the health of employees and business practice. Work-related stress is a major issue that has been recognized by the World Health Organization (WHO and ILO, 2022) [1], where work-related stress impacts employees’ well-being and organizational effectiveness.

While a variety of strategies to manage stress have been investigated in culturally bound Western settings, little is known about how they translate to non-Western settings and to a greater extent to higher education, namely, Saudi higher education. In Saudi universities, the organizational upheaval under Vision 2030 has raised the pressure on academics and administrators. Restructured workloads, increased accountability, and increased productivity expectations have forced workers to adjust. Consequently, stress management has become an integral aspect of everyday life rather than a rare challenge.

The empirical evidence on the employee perception and response to stress-management initiatives in Saudi higher education institutions is fairly limited despite this phenomenon. This lacuna is particularly relevant as the perceptions of organizational interventions are shaped by cultural norms, hierarchical decisions, and collectivistic work values (Yousef, 2024 [2]; Mansour and Abu Tayeh, 2024 [3]). Prior research has underscored the need to know how employees perceive job demands and how coping resources are mobilized under stress. However, little attention has been paid to these processes in Saudi universities undergoing fast-changing institutional contexts. As a result, the context of the stress-management strategies in this setting has thus far not been well illustrated.

In response to this background, the current study examines the associations between four workplace stress-management interventions (mindfulness, time-management training, scheduling autonomy, and coworker support) and the individual’s perceived stress, job performance, and work–life balance at a Saudi university. A mixed-methods approach was applied to integrate both quantitative and qualitative analysis to gain a more contextual, situated understanding of the nature of implementation between the interventions. Drawing on existing theoretical approaches within the context of Saudi higher education, the study aims to contribute to global research and produce valuable knowledge by providing practical analysis for institutional authorities seeking to improve organizational wellness and support from an employee well-being perspective and/or organizational sustainability.

2. Literature Review

This section examines the theoretical underpinnings and contemporary research regarding workplace stress and focused treatments that influence employee performance and well-being in higher education. However, further attention is given to why these measurements in this work are consonant with those theoretical roots in order to perform a strong structural analysis.

2.1. Theoretical Framework

Workplace stress for the well-being of the workforce, the job performance of the workers, and the survival of the organization in which employees work constitutes a complex and widespread occupational issue. In the context of higher education institutions, stress is amplified through competitive internal role standards, perpetual performance reviews or performance evaluations and increased institutional demands (Demerouti et al., 2001) [4], and escalating pressures of institutionalization. Pressures such as these require employees to persistently modify their coping styles, and thus, stress-reducing measures are one of the basic factors influencing organizational life. Stress is understood by the Transactional Stress and Coping Model to be a result of social interaction between individuals and their environment. Stress is not caused by external environmental forces; it is seen as cognitive appraisals of situational demands by individuals. Stress, according to Lazarus and Folkman (1984), is when the individuals believe the required environment exceeds its resourcing of coping resources [5]. This process begins with making an appraisal of a potential threat, prior to an appraisal of some coping responses that activate coping actions. In order to meet this complexity, coping strategies which may serve a dual purpose are problem-focused (address the origin of the stressor) and emotion-focused (reflection is key for emotional control) (Lazarus and Folkman, 1984) [5]. In such a framework, stress-reduction interventions (e.g., the practice of mindfulness) have been suggested to moderate impacts of stress on appraisal processes, emotional awareness, adaptive stress responses, and others by promoting the process of appraisal. According to this model, coping efficacy is also contextual: cultural and organizational factors are significant predictors of intervention effectiveness. Based on cognitive and emotional processes, the Transactional Model shows us how many factors people perform, while the Job Demands-Resources (JDs-Rs) framework gives us a wider viewpoint, working from society’s viewpoint. With regard to the JDs–Rs framework, this perspective highlights how psychological health is influenced by work constraints, whether the pressures of daily life are material demands or not. Job expectations, such as workload size or time pressures resulting from job conflict and the resources available to employees, have an influence on employee well-being performance. Furthermore, more than organizational resources, the JDs–Rs model considers personal resources too (skills/capabilities—personal attributes to promote resilience and help achieve goals). The employee has time-management skills through which they do not have to face being overwhelmed or chronically under stress but rather are able to handle challenges and perform under pressure. Additionally, the JDs–Rs approach continues to propose the gain spiral process, through which personal resources contribute to gaining more structural and social resources over time, leading to positive outcomes (Bakker and Demerouti, 2007) [6]. Workers who sustain such gain spirals demonstrate lower burnout and strain and are better able to self-regulate and work efficiently. Good time management results in schedule management, the capacity for persistent performance, and effective participation with others. These, as well as cooperation such as work-related peer support, are protective against workload and contribute to building emotional toughness and a collegial working environment (Karasek et al., 1998) [7]. The Transactional Model of Stress and Coping, integrated with the JDs–Rs framework, provides a comprehensive perspective on organizational stress in higher education. The transactional approach elaborates employees’ perceptions of cognitive and emotional stressors, while the JDs–Rs framework helps to understand how these experiences and their outcomes are influenced, in part, by individual, social, and structural resources. In combination, these constructs provide a coherent picture of the contribution mindfulness, time management, scheduling control, and coworker support make together in predicting the degree of perceived stress, job performance, and work–life balance in academic institutions experiencing dynamic organizational contexts (WHO and ILO, 2022) [1]. Expanding on these theoretical underpinnings, the current study sets up a measurement framework that resonates with the intervention’s rationale and proposes a structural equation modeling (SEM) framework. The constructs were derived from well-established instruments frequently employed to operationalize essential components of the Transactional Model of Stress and Coping and the Job Demands–Resources framework. Mindfulness was assessed by the Mindful Attention Awareness Scale (MAAS) (Brown and Ryan, 2003) [8], a standard scale used to measure the participant’s awareness of and attention towards present-moment experiences. Perceived stress was measured with the Perceived Stress Scale (PSS, Cohen et al., 1983) [9].

This scale shows how employees feel about the stress they experience at work. The Time-Management Behavior Scale (TMBS) developed by Macan et al. (1990) [10] and further refined by Macan (1994) [11] operationalized time management by evaluating planning, prioritization, and perceived control over time utilization.

The scheduling autonomy scale created by Thomas and Ganster (1995) [12] was used to measure schedule control. This scale shows how much control employees think they have over when and how their work tasks are performed. The coworker support subscale of the Job Content Questionnaire (JCQ-CS) created by Karasek et al. (1998) [7] was used to measure coworker support. This subscale looks at how people think their coworkers are helping them emotionally and practically. Job performance was quantified using the in-role job performance scale created by Williams and Anderson (1991) [13], whereas work–life balance was assessed through the short-form work–life balance scale introduced by Brough et al. (2014) [14].

The study establishes conceptual coherence among the theoretical framework, the proposed relationships, and the empirical analysis conducted using SEM by anchoring the measurement model in theoretically sound and empirically validated instruments.

These frameworks have been extensively utilized in various organizational contexts; however, their functionality is not culturally neutral. Previous studies indicate that the efficacy of stress-management resources may differ across cultural and institutional contexts, especially in collectivist and high-context societies (Hofstede, 2001 [15]; Ali, 1988 [16]).

In higher education institutions of Saudi Arabia where religious aspects, a hierarchical structure, and collective norms dictate work behaviors, the activation and interpretation of psychological and social resources may diverge from Western contexts. This research applies these paradigms as analytical lenses while remaining sensitive to the cultural and institutional attributes of the Saudi academic context that are elaborated in the discussion.

2.2. Hypothesis Development

Based on the integrated theoretical framework developed in Section 2.1, the present study posits a series of hypotheses to explain the relationship between psychological, personal, social, and structural resources in shaping employee stress, performance, and work–life balance. Informed by the Transactional Model of Stress and Coping and the Job Demands–Resources (JDs–Rs) framework, the hypotheses suggest that stress-management interventions have direct and indirect pathways through which they affect employee outcomes in higher education institutions.

2.2.1. Mindfulness and Perceived Stress (H1)

In the Transactional Model of Stress and Coping, stress is conceptualized as a function of individuals’ cognitive appraisal of environmental demands and their perceived coping capacity rather than as a direct outcome of objective stressors alone (Lazarus and Folkman, 1984) [5]. As a psychological resource for managing stress, mindfulness is suggested to impact stress outcomes by enhancing present-moment awareness, reducing automatic reactivity, and supporting adaptive emotional regulation (Brown and Ryan, 2003) [8].

There is a wealth of empirical evidence for the stress-reduction impact of mindfulness-based techniques in occupational and academic contexts. Meta-analyses consistently demonstrate negative associations between mindfulness and perceived stress, anxiety, and emotional exhaustion (Khoury et al., 2015; Vonderlin et al., 2020; Hülsheger et al., 2013) [17,18,19].

Furthermore, Alzahrani et al. (2023) [20] indicate that mindfulness-based programs can reduce stress among university populations in Saudi Arabia, attesting to their relevance within the local academic context.

Through the mediation of cognitive appraisal and the adaptation of emotion-focused coping strategies, mindfulness is anticipated to lead to a decrease in self-reported stress among employees in higher education institutions.

H1. 

Mindfulness is negatively associated with perceived stress.

2.2.2. Time Management and Job Performance (H2)

Time management is a key personal resource that facilitates employees’ regulation of workloads, prioritization, and perceived control over job demands (Demerouti et al., 2001) [4] within the JDs–Rs framework. By effectively managing these tasks, time management leads to less inefficiency and cognitive load as well as more engagement and sustained task performance. Previous research has consistently demonstrated a positive correlation between time-management habits such as planning, prioritization, and goal setting and in-role job performance, particularly in knowledge-intensive and self-regulated work contexts such as higher education (Macan, 1994; Aeon et al., 2021) [11,21]. Employees with effective time-management skills are positively associated with achieving task persistence, meeting deadlines, and being productive under stress.

Within the JD–Rs framework, effective time management is understood as a personal resource that supports sustained effort by helping employees regulate their work demands and limit excessive strain, which may be reflected in stronger task commitment and performance.

H2. 

Time management and job performance are positively related.

2.2.3. Time Management and Coworker Support (H3)

Time management does not only make tasks go more smoothly; it can also change how coworkers get along with each other at work. According to social exchange theory, when workers are on time and use their time in a predictable way, this consistency helps coworkers work together and trust each other (Kelly, 2002; Peeters and Rutte, 2005) [22,23]. As per the JDs–Rs model, coworker support is a type of social resource that may derive from other personal resources. According to the theoretical model, organized and efficient time management is associated with perceived coworker support and interpersonal collaboration, reduced last-minute disruptions, and enhanced collective planning.

H3. 

Time management is positively related to coworker support.

2.2.4. Coworker Support and Job Performance (H4)

Coworker support has long been discussed in the literature as an important element of the social environment at work. From the perspective of the Job Demands–Resources model, such support becomes particularly relevant when employees are required to deal with high levels of workload or time pressure. In practice, employees rarely complete all of their tasks completely on their own. When work becomes heavier, they tend to turn to coworkers for clarification, practical help, or simply reassurance. These exchanges are part of everyday work life and are often taken for granted. Even so, these factors shape how work unfolds in practice, how smoothly tasks progress, and how effectively results are achieved.

Early work on psychosocial working conditions pointed to supportive relationships as a key way of coping with demanding work environments (Karasek et al., 1998) [7]. More recent work has shifted attention toward the role of coworker support in interdependent work settings, where completing tasks depends on ongoing interaction among colleagues. In these settings, performance depends on more than individual effort alone. It is also influenced by the extent to which coworkers are willing to share information, offer help when it is needed, and adjust their work in relation to others. As Chiaburu and Harrison (2008) [24] observed, employees who receive stronger support from their coworkers tend to perform better, in part because work is more smoothly coordinated and everyday disruptions are less frequent. Viewed this way, coworker support helps sustain work processes, especially when demands are high.

H4. 

Coworker support is positively associated with job performance among employees.

2.2.5. Time Management and Schedule Control (H5)

Schedule control refers to the degree to which employees are able to influence when and how their work tasks are performed (Thomas and Ganster, 1995) [12]. Many discussions focus on the role of organizational policies and formal rules. Employees’ experiences suggest that schedule control is understood in more practical terms. Rather than being confined to formal agreements, it often develops through the ways people organize their work from day to day. How time is handled in everyday situations can make a real difference. When employees think ahead, they are better able to prepare for busier moments and adjust their efforts along the way. This can make work feel more manageable and help prevent pressure from gradually building. Preparing tasks in advance and shifting effort when necessary allows the workday to run more smoothly, even when formal schedules remain unchanged.

Prior research supports this view by showing that stronger time-management skills are associated with higher levels of perceived schedule autonomy (Hafner et al., 2010; Aeon and Aguinis, 2017) [25,26]. In this sense, time management appears to function as a practical means through which employees can gain greater influence over their work schedules, even in structured work environments.

H5. 

Time management is positively related to schedule control.

2.2.6. Job Performance and Work–Life Balance (H6)

Work–life balance refers to an individual’s capacity to meet work and personal role demands with minimal strain (Brough et al., 2014) [14]. Enrichment theory suggests that success in one life domain can generate resources—such as time efficiency, positive affect, and self-efficacy—that may be applied to improve functioning in other domains (Greenhaus and Powell, 2006) [27]. Within the JDs–Rs framework, job performance can therefore be viewed not only as an outcome of work processes but also as a resource that supports psychological detachment, recovery from work-related stress, and the management of work and non-work roles.

High levels of job performance may thus contribute to the accumulation of transferable resources that help individuals maintain balance beyond the work domain.

H6. 

Job performance is positively related to work–life balance.

2.2.7. The Mediating Role of Job Performance (H7)

Finally, over time, personal skills can set in motion a sequence of positive outcomes that reinforce one another (Bakker and Demerouti, 2007) [6]. In this study, time management appeared to play such a role by helping employees perform their work more effectively. When tasks are completed on time and workloads are better organized, employees experience less ongoing pressure and fewer unfinished responsibilities, which can make it easier to manage competing work and non-work roles.

Prior research supports the mediating role of job performance in linking time-management behaviors to broader well-being outcomes (Macan, 1994; Claessens et al., 2007; Aeon et al., 2021) [11,21,28].

Within the JDs–Rs framework, job performance functions as a proximal outcome through which time management translates personal efficiency gains into reduced role strain and enhanced work–life balance.

H7. 

Job performance mediates the relationship between time management and work–life balance.

2.3. Conceptual Framework

The conceptual framework integrates the Transactional Model of Stress and Coping (Lazarus and Folkman, 1984 [5]) with the Job Demands–Resources (JDs–Rs) model (Demerouti et al., 2001 [4]; Bakker and Demerouti, 2007 [6]) to explain how personal (mindfulness), skill-based (time management), structural (schedule control), and social (coworker support) resources influence employee outcomes—stress, job performance, and work–life balance—in Saudi higher education.

Mindfulness is conceptualized as a coping-related psychological resource expected to reduce perceived stress (H1), whereas time management functions as a skill-based personal resource that enhances job performance (H2) and schedule control (H5). Time management is also proposed to strengthen coworker support (H3), which subsequently contributes positively to job performance (H4). Job performance is hypothesized to have a direct positive effect on work–life balance (H6), while time management is expected to influence work–life balance indirectly through job performance (H7).

Figure 1 presents how different types of resources are connected and how they operate together in everyday work settings. In Saudi universities, where working conditions are shaped by organizational hierarchies, collective norms, and recent reforms associated with Vision 2030, these connections may influence how employees make sense of work demands and manage stress.

The framework thus provides a clear theoretical rationale for examining whether relationships established in Western contexts hold within the Saudi higher education environment.

3. Materials and Methods

A smaller group of participants was selected for the interview phase in order to capture variation in gender, job roles, and levels of experience. A cross-sectional design was used to identify measurable patterns, while semi-structured interviews explored how employees described their experiences of these interventions within their work context (Creswell and Plano Clark, 2018) [29]. The survey data were then used to explore how different types of resources—personal, skill-based, structural, and social—were related to one another. Structural equation modeling was used to examine the proposed relationships simultaneously (Kline, 2016) [30]. By contrast, the interview materials offered a more contextualized understanding of how employees made sense of these resources and how they were applied within actual work environments.

The study was conducted at Tayma University College, and the participants were academic and administrative staff during a period of organizational transformation. In order to gauge the main dimensions of interest, established measurement instruments were employed to assess mindfulness, perceived stress, time management, schedule control, coworker support, in-role job performance, and work–life balance. Based on the same theoretical assumptions, interview questions were developed to allow the qualitative findings to be meaningfully connected to the quantitative analysis (Braun and Clarke, 2006) [31].

3.1. Participants and Sampling

This study was conducted with 124 participants. In total, 124 individuals participated in either the survey, the interviews, or both components of the mixed-methods design. Participants included academic and administrative staff from Tayma University College. Following invitations distributed through institutional mailing lists and coordination with departments, 104 employees participated in the survey component. Given the number of constructs included and the complexity of the analytical model, the survey sample size was considered appropriate for examining the proposed relationships (Kline, 2016) [30].

A smaller sample was selected for the interview phase to ensure diversity in gender, job roles, and levels of experience. This approach made it possible to capture a range of perspectives while also allowing for sufficient depth in the interview data (Guest, Bunce, and Johnson, 2006) [32]. The interview material was subsequently used to aid the interpretation of the survey findings, particularly by illustrating how employees linked their everyday work experiences to stress-management initiatives. Employees were eligible to participate only if they had worked in the organization for at least six months, ensuring familiarity with the work context. Participation was voluntary, responses were anonymous, and no incentives were offered.

3.2. Tools and Measurements

Data were collected through an online questionnaire. The focus was on a small number of concepts that were central to the study. For this reason, the questionnaire was kept deliberately short. It consisted of a limited set of statements that captured the main variables of interest rather than using an extensive measurement instrument. In total, the survey included 28 statements. Each construct was represented by four items, covering seven thematic areas. Participants were asked to indicate how much they agreed with each statement using a five-point scale. The questionnaire was based on widely used measurement instruments in the existing literature. Items derived from the Mindful Attention Awareness Scale (Brown and Ryan, 2003) [8] were used to measure mindfulness. Perceived stress was, in turn, measured with items from the Perceived Stress Scale (Cohen et al., 1983) [9]. In addition to these essential constructs, the questionnaire contained items on daily work experience and outcomes. Time management was assessed with items adapted from the Time-Management Behavior Scale (Macan, 1994) [11], and perceived control over work schedules was measured by items derived from the Schedule Autonomy Scale (Thomas and Ganster, 1995) [12].

Items from the coworker support subscale of the Job Content Questionnaire (Karasek et al., 1998) [7] were used to measure coworker support. Job performance was evaluated using in-role performance measures adapted from Williams and Anderson (1991) [13], and work–life balance was measured using the brief work–life balance scale (Brough et al., 2014) [14].

Each construct was treated as a reflective measure. For clarity in reporting, a short label was assigned to each one. Items that were phrased in a negative direction were recoded so that higher scores consistently indicated higher levels of the construct.

The survey was distributed through a secure, institution-based platform. The invitation message explained the aim of the study. It also made clear that all responses would be treated as confidential. Participants were informed about the length of the questionnaire. Completing it was expected to take about 8 to 10 min. To remind employees about the survey, two follow-up messages were sent. These were sent one week apart.

3.3. Collecting Qualitative Data

The interviews were semi-structured. Most of them lasted about forty-five minutes. A flexible interview guide was used. Its purpose was to keep the discussion on the main topics of the study. At the same time, participants were free to talk about their own experiences in their own way. Interviews were conducted in either Arabic or English. The interview format was chosen based on what each participant felt most comfortable with. All interviews were recorded after participants agreed to take part, and the recordings were later transcribed by professional transcribers. Notes were also taken during the interviews, mainly to capture contextual details and relevant nonverbal cues.

The survey data were analyzed using structural equation modeling to examine how the main study variables were related. The interview material was then revisited to better understand how these patterns appeared in everyday work settings. When participants spoke about schedule control and work–life balance, they often mentioned flexible working hours and reduced commuting demands. In contrast, when mindfulness practices were described as less helpful, participants frequently referred to cultural expectations and heavy workloads as limiting factors. Overall, the qualitative component served to deepen and clarify the interpretation of the quantitative findings. It was not intended to function as a separate exploratory stage. Interviews were used to explain the relationships tested in the SEM model. Together, the two forms of data allowed for a clearer and more context-sensitive interpretation of both significant and non-significant findings (Creswell and Plano Clark, 2018; Fetters, Curry, and Creswell, 2013) [29,33].

3.4. Quantitative Assessment

Initial descriptive checks and basic diagnostics were conducted using SPSS 26. The main analytical procedures were then carried out using AMOS 22. These included confirmatory factor analysis and the testing of the proposed structural relationships.

Evaluation of the Measurement Model

Analysis began with the specification of a measurement model that included seven latent factors. This model was estimated using maximum likelihood procedures. Several indicators were examined to assess how well the model fit the data. These included incremental fit indices, such as the Comparative Fit Index and the Tucker–Lewis Index, as well as error-based indicators, including the Root Mean Square Error of Approximation and the Root Mean Square Residual. Evaluation was guided by the relevant literature that provides established guidelines (Hu and Bentler, 1999) [34]. A few constructs were represented with limited indicators. Model identification was conducted by fixing one loading, and loadings were verified with acceptable levels and low error variance. Modification indices were carefully examined, and correlated residuals only were introduced where a theoretical rationale for use was apparent in the same construct. Other checks were conducted to examine the quality of the measurement model. Composite reliability and average variance extracted values provided further evidence regarding internal consistency and convergent validity that supported adequate construct measurement. Discriminant validity was evaluated with common comparison criteria (the Fornell–Larcker criterion) and the heterotrait–monotrait ratio, and results were interpreted according to standard thresholds.

Testing the Structural Model

A structural model was then specified to examine hypothesized relationships between the selected study variables:

• Mindfulness → stress (H1);
• Time management → job performance (H2);
• Time management → coworker support (H3);
• Coworker support → job performance (H4);
• Time management → control of the schedule (H5);
• Job performance → balance between work and life (H6);
• Time management → work–life balance (indirectly through job performance; H7).

Consistent with established methodological advice (Preacher and Hayes, 2008) [35], indirect relationships in the model were evaluated by applying a bias-corrected bootstrapping resampling technique. Using this approach, the analysis examined how effects moved across the different variables. Results are presented using standardized estimates, along with information on their precision and the share of variance explained in the outcome variables. Model interpretation followed established guidelines for structural equation modeling (Hu and Bentler, 1999; Kline, 2016) [30,34].

3.5. Qualitative Study

The interview data were analyzed using thematic analysis, following the approach described by Braun and Clarke (2006) [31]. The analysis focused on themes that appeared repeatedly across participants’ accounts and began with a small set of themes closely related to the main focus of the study. As the analysis progressed, additional themes were allowed to emerge based on how participants described their own experiences.

Two trained researchers independently reviewed the transcripts to increase the rigor of the analyses. Differences in interpretation were discussed until common agreement was reached. Qualitative analysis software (NVivo 15) was used to organize and help code the data, and agreement among the coders was monitored for consistency in theme interpretation. After that, the themes emerging from the interviews were examined alongside the quantitative findings, to show how the statistical relationships found in the analysis translated into practice.

The interviews offered, for example, reports on how routine time-management habits led to relationships of trust and collaboration between employees, and how limited engagement with mindfulness practices curtailed their effectiveness for alleviating stress.

3.6. Ethical Considerations

The study was approved by the University of Tabuk Institutional Review Board before data collection was carried out. Participation was voluntary for all concerned, and all participants provided written informed consent. The questionnaire did not collect any identifiable information, and demographic questions were optional. Data were only available to the researchers and were kept on password-protected, secure servers. Subjects were informed that they could withdraw from the study at any time without penalty.

3.7. Methodological Rigor

Consideration was also paid to participants’ accounts along the course of the research process. Instead of extensive or multilayered instrumentation, a few items were applied to each construct to minimize respondent overload and yet attain essential dimensions. This strategy allowed avoiding overload of participants and resulted in stable and reliable analytic results.

Measurement instruments were chosen due to their pre-established reliability and demonstrated validity in previous work as well as their common application across different research contexts. Adopting these validated scales also enabled meaningful comparisons of results here with results from previous studies (Brown and Ryan, 2003; Cohen et al., 1983; Macan, 1994; Thomas and Ganster, 1995; Karasek et al., 1998; Williams and Anderson, 1991; Brough et al., 2014) [7,8,9,11,12,13,14].

A mixed-methods design was used to allow for the examination of secondary themes by combining survey records with inter-textual material, i.e., through survey data and interview material. While the survey technique can be said to illustrate general patterns across study variables, interviews would give a more in-depth approach into how the phenomena manifested in day-to-day working environments. Incorporation of both data sources created a more understandable and meaningful interpretation of the conclusions.

4. Results

4.1. Quantitative Results

4.1.1. Reliability and Descriptive Analysis

As shown in

Table 1. Descriptive statistics and normality tests of key variables.

Construct	Items	Mean (Range)	SD (Range)	Skewness (Range)	Kurtosis (Range)
Mindfulness (MN1–MN4)	4	3.04–3.75	1.13–1.34	−0.849 to −0.012	−1.014 to 0.248
Time Management (TM1–TM4)	4	3.20–3.96	0.97–1.37	−1.113 to −0.234	−1.166 to 1.565
Schedule Control (SC1–SC4)	4	2.97–3.88	1.00–1.27	−0.964 to −0.121	−0.997 to 0.885
Stress Perception (PS1–PS4)	4	3.33–3.91	0.97–1.21	−1.205 to −0.425	−0.687 to 1.941
Coworker Support (CS1–CS4)	4	2.94–4.18	0.79–1.28	−1.279 to −0.059	−1.103 to 3.208
Job Performance (JP1–JP4)	4	4.45–4.46	0.69–0.76	−1.783 to −1.063	0.565 to 4.351
Work–Life Balance (WLB1–WLB4)	4	4.39–4.53	0.67–0.89	−1.893 to −0.925	−0.285 to 3.981

, the mean scores for all study variables ranged from moderate to high (M = 2.94–4.53), indicating generally positive work experiences among participants.

The mindfulness items (MN1–MN4) yielded moderate average scores (M = 3.04–3.75, SD = 1.13–1.34). This suggests that employees were reasonably aware of their work environment and their own emotional states. However, mindfulness did not emerge at particularly high levels, nor was it absent. Rather, it appeared to be part of everyday work routines without yet functioning as a consistent strategy for managing work-related pressure.

A comparable pattern was observed for time management (TM1–TM4), with mean scores falling within a moderate and relatively stable range (M = 3.20–3.96, SD = 0.97–1.37). At the same time, clear variation was evident in how employees organized, prioritized, and planned their work. Such differences were expected, as previous research has shown that time-management practices vary across job roles and depend on individual self-regulation at work (Macan, 1994; Aeon et al., 2021) [11,21].

Perceptions of schedule control showed greater dispersion. Responses to the schedule control items varied noticeably. Some aspects were rated lower than others. For instance, SC3 received the lowest average score (M = 2.97, SD = 1.27), while SC4 was evaluated more positively (M = 3.88, SD = 1.00). These differences suggest that employees did not experience schedule control in the same way. For some, organizing their work schedule felt reasonably flexible. For others, however, there was little room to influence how their time was arranged. In everyday terms, flexibility was something some employees could rely on, while others had little access to it.

Most of the answers were between 3.33 and 3.91, which is the middle range of stress levels. This means that stress was a part of work life, but it was not something that employees felt all the time or very strongly. Instead, pressure came and went, showing up at certain times instead of being a constant part of work. For the most part, employees felt able to deal with these situations as they arose. This reading fits well with the Transactional Model of Stress and Coping, which emphasizes the importance of how individuals interpret and respond to demands (Lazarus and Folkman, 1984) [5]. Using both positively and negatively phrased items made this pattern clearer, as lower stress was reflected in agreement with positive statements and disagreement with negative ones.

Outcome-related ratings were, overall, more positive. In particular, job performance items (JP1–JP4) received consistently high scores, with mean values ranging from 4.45 to 4.46 (SD ≈ 0.70). This indicates that most participants felt they were performing their jobs effectively and meeting their work responsibilities. Work–life balance (WLB1–WLB4) was also rated positively (M = 4.39–4.53, SD = 0.67–0.89). This indicates that many respondents felt able to manage the boundaries between their work and personal lives in a satisfactory way.

Greater variation was observed for coworker support (CS1–CS4). While CS1 to CS3 received relatively high ratings (M ≈ 4.14–4.18), CS4 was rated notably lower (M = 2.94, SD = 1.28). This difference suggests that although coworkers were generally perceived as supportive, certain conditions may have limited consistent mutual assistance. Similar inconsistencies have also been observed in academic settings. In these environments, employees often have to manage both administrative and academic duties at the same time, which can make consistent support more difficult (Chiaburu and Harrison, 2008; Yousef, 2024) [2,24].

To check whether the data met basic distributional assumptions, skewness and kurtosis values were examined using commonly accepted guidelines (George and Mallery, 2010; West et al., 1995) [36,37]. As reported in Table 1, skewness values ranged from −1.893 for work–life balance to −0.012 for mindfulness. Kurtosis values ranged from −1.166 for time management to 4.351 for job performance. Taken together, these results suggested that the data showed acceptable levels of normality and were suitable for the analyses that followed. These indices demonstrate that all observed variables closely align with a normal distribution, hence validating the use of parametric statistical methods, including SEM estimates.

Next, a reliability analysis was conducted to see how consistent each construct was on the inside.

Table 2. Reliability analysis (Cronbach’s alpha).

Construct	Items	Cronbach’s Alpha
Mindfulness	4	0.785
Time Management	4	0.719
Schedule Control	4	0.776
Stress Perception	4	0.720
Coworker Support	4	0.701
Job Performance	4	0.938
Work–Life Balance	4	0.858

shows that Cronbach’s alpha values ranged from 0.701 to 0.938, which means that all of the measures were reliable to some degree. In particular, mindfulness (α = 0.785), time management (α = 0.719), schedule control (α = 0.776), perceived stress (α = 0.720), coworker support (α = 0.701), job performance (α = 0.938), and work–life balance (α = 0.858) all surpassed the established criterion of α ≥ 0.70, which is recommended for psychological and organizational research (George and Mallery, 2010) [36]. The significantly high reliability of the job performance and work–life balance scales indicate consistent perceptions among participants; however, the comparatively lower alpha for colleague support underscores the intrinsic variability in interpersonal dynamics in workplace environments.

In conclusion, these results show that the measurement tools used are psychometrically sound and acceptable for the research population (George and Mallery, 2010) [36].

A paired-samples t-test was conducted to examine the pre- and post-intervention dynamics of subjective stress.

Table 3. Descriptive statistics and paired-samples test of perceived stress (PSSB and PSSA).

Measure	N	Mean	SD	Min	Max	r	t(103)	p
PSSB (Before)	104	2.99	1.36	1	5
PSSA (After)	104	3.41	1.03	1	5	0.469	–3.426	0.001

Note: N = sample size; SD = standard deviation; r = correlation between PSSB and PSSA; p < 0.01.

shows that the mean perceived stress before the intervention (PSSB) was M = 2.99, SD = 1.36. After the intervention (PSSA), it went up a little to M = 3.41, SD = 1.03. The two measures had a moderate correlation (r = 0.469, p < 0.001), and the difference was statistically significant (t(103) = −3.426, p =0.001). The rise in reported stress was unexpected. It did not mean that employees were necessarily worse off. Earlier research shows that mindfulness can make people notice stress more clearly. This is often called the awareness paradox. Stress feels more visible, not stronger (Malinowski and Lim, 2015; Donald et al., 2019).

Taken together, the descriptive and reliability results show that most participants held generally positive views of the interventions that were implemented and the work resources that were available. Most participants reported that their job performance and work–life balance remained relatively stable and that their stress levels were not particularly high. The findings also point to an important limitation of short-term evaluations.

Changes in work-related stress do not always become apparent immediately. Instead, they may develop gradually over time and become more complex. The Transactional Model of Stress and Coping emphasizes that individuals’ appraisals of and responses to demands shape their experience of stress (Lazarus and Folkman, 1984) [5]. Viewed in this way, the results are consistent with this model.

The findings also fit with the Job Demands–Resources framework, which points to the importance of having enough resources, not only at the individual level but also within the organization as a whole (Bakker and Demerouti, 2007; Cooper and Quick, 2017) [6,38]. Taken together, these perspectives point to the limits of relying on individual coping alone when dealing with workplace stress. The broader organizational context, including the types of support employees receive in their daily work, also affects how they feel and deal with stress.

4.1.2. Exploratory Factor Analysis

The first step in the analysis was to look at how the questionnaire items related to one another. An exploratory factor analysis was used for this purpose, drawing on all items included in the survey. These items were designed to capture seven main areas: mindfulness, time management, schedule control, perceived stress, coworker support, job performance, and work–life balance. At this stage, the goal was simply to check whether the items worked together in a meaningful way and reflected the concepts they were intended to measure. In general, the pattern that emerged was consistent with theoretical expectations, which supported moving forward with the analysis. Before running the factor analysis, the data were examined to ensure that this step was appropriate. Attention was given to both sample adequacy and the degree of association among the items. As shown in

Table 4. Summary of exploratory factor analysis results for key constructs.

Construct	KMO	Bartlett’s Test (p)	Variance Explained (%)	Strongest Loading	Weakest Loading
Mindfulness (MN1–MN4)	0.743	<0.001	61.4	MN4 = 0.871	MN3 = 0.587
Time Management (TM1–TM4)	0.707	<0.001	58.0	TM2 = 0.847	TM4 = 0.472
Schedule Control (SC1–SC4)	0.760	<0.001	63.2	SC2 = 0.885	SC3 = 0.530
Perceived Stress (PS1–PS4)	0.741	<0.001	57.9	PS3 = 0.849	PS4 = 0.425
Coworker Support (CS1–CS4)	0.752	<0.001	69.2	CS2 = 0.965	CS4 = 0.151
Job Performance (JP1–JP4)	0.851	<0.001	84.4	JP3 = 0.953	JP1 = 0.881
Work–Life Balance (WLB1–WLB4)	0.823	<0.001	71.3	WLB1 = 0.866	WLB4 = 0.822

, the Kaiser–Meyer–Olkin value was 0.83, well above the commonly accepted threshold of 0.60 (Kaiser, 1974). Bartlett’s Test of Sphericity was highly significant (χ² = 2217.88, df = 378, p < 0.001), suggesting that the items were related closely enough to be examined together. Taken as a whole, these initial checks gave sufficient confidence to proceed with exploratory factor analysis. The individual EFAs provided robust evidence of factorial integrity across constructs. Table 4 shows that KMO values ranged from 0.707 to 0.851, and all of Bartlett’s tests were significant at p < 0.001, which means that the correlations between items were strong enough to allow for factor extraction. The percentage explained varied ranged from 57.9% for perceived stress to 84.4% for job performance. This means that each factor explained a large part of the shared variance in its indicators.

Most of the factor loadings were strong, with most of them being λ ≥ 0.60. This showed that the factors were convergent and coherent within themselves. Three items performed less strongly than the others: TM4 (time management), PS4 (perceived stress), and CS4 (coworker support). Among these, CS4 showed the lowest factor loading (λ = 0.151) as well as the lowest communality. This may indicate that the item was less clearly interpreted by participants or that it aligned weakly with the other indicators of coworker support. Despite this, the items were retained at this preliminary stage in order to preserve theoretical coverage and to allow confirmatory factor analysis (CFA) to apply more stringent criteria for validation, in line with recommendations by Kline (2016) [30].

The scale included different kinds of statements. Some focused on stress and loss of control. Others reflected more positive experiences, such as feeling that things were going well. Using both types made the responses more balanced. It also reduced the tendency to agree with items automatically, which is a common problem when questions are phrased in only one direction (Podsakoff et al., 2003) [39].

Nonetheless, prior psychometric studies have observed that reverse-coded items frequently yield reduced factor loadings due to variations in cognitive processing rather than genuine conceptual inadequacies (Marsh, 1996; Wong, Rindfleisch, and Burroughs, 2003) [40,41]. The reduced loading of PS4 in the present analysis therefore reflects a recognized methodological phenomenon rather than a substantive limitation in the measurement of perceived stress. Furthermore, the inclusion of such items can enhance content validity and broaden construct coverage (Netemeyer, Bearden, and Sharma, 2003; Taylor, 2015) [42,43].

The lower loading for CS4 in coworker support may also show that employees see peer help differently in hierarchical or mixed administrative–academic settings. In certain organizational cultures, social support is more implicit or conditional, which makes reactions less consistent (Chiaburu and Harrison, 2008) [24]. It is important to keep this kind of diversity for confirmatory analysis since it could show important sub-dimensions or culturally ingrained differences during CFA modeling. The EFA results show significant early signs of factorial validity and conceptual clarity across the seven constructs as a whole. The distinct loading patterns, significant explained variation, and satisfactory KMO values collectively demonstrate that the items effectively represent the desired theoretical features of the stress-management framework.

As a result, these results support the move to confirmatory factor analysis (CFA) to thoroughly check the model fit, look at convergent and discriminant validity, and improve item structure before comprehensive structural equation modeling (SEM). The CFA phase will ascertain if the less robust indicators (TM4, PS4, CS4) necessitate exclusion or modification and whether the proposed seven-factor model sufficiently encapsulates the data structure within the framework of Tayma University College.

4.1.3. Confirmatory Factor Analysis

To confirm the factor structure found in the exploratory study, a confirmatory factor study (CFA) was conducted with AMOS 22 to test the proposed seven-construct measurement model. The model included 28 observable indicators that represented mindfulness, time management, schedule control, perceived stress, coworker support, job performance, and work–life balance, as stated in the theoretical framework. Figure 1 shows how each indicator was set up to load on its appropriate latent component.

All models were estimated using the maximum likelihood (ML) approach, in accordance with guidelines for SEM estimation under approximate multivariate normality (Kline, 2016) [30]. The model was bootstrapped with 5000 bias-corrected (BC) resamples to make it stronger and protect it from possible departures from normality. This method produced empirical confidence intervals and BC-adjusted p-values, yielding a more dependable evaluation of the statistical significance of factor loadings and path coefficients (Byrne, 2016; Hu and Bentler, 1999; Hair et al., 2019) [34,44,45].

• First CFA Model

The results from the first CFA showed that most of the factor loadings were statistically significant, especially for job performance, work–life balance, and mindfulness (see Figure 2). However, the global model fit indices did not reach acceptable levels (see

Table 5. Model fit indices for the initial CFA model.

Fit Index	Recommended Threshold	Obtained Value	Interpretation
χ2 (Chi-square)	Non-significant (sample-size-sensitive)	812.599, df = 341, p < 0.001	Significant
χ2/df	≤3.0, acceptable	2.383	Acceptable
CFI	≥0.90 (good); ≥0.95 (excellent)	0.774	Poor fit
TLI	≥0.90	0.749	Poor fit
IFI	≥0.90	0.778	Poor fit
GFI	≥0.90	0.633	Weak fit
RMSEA (90% CI)	≤0.08, acceptable; ≤0.05, excellent	0.116 (0.106–0.126)	Poor fit
PCLOSE	>0.05	0.000	Poor fit

). The model produced χ2(341) = 812.60, p < 0.001, with χ2/df = 2.383, CFI = 0.774, TLI = 0.749, IFI = 0.778, GFI = 0.633, and RMSEA = 0.116 (90% CI [0.102, 0.130]). The χ²/df ratio was within the acceptable range (≤3.0), but other measures, especially the CFI, TLI, and RMSEA, showed that the model did not fit well overall. Hu and Bentler (1999) [34] say that a good fit usually needs the CFI and TLI to be at least 0.90 and the RMSEA to be at most 0.08.

These results indicated model misspecification, potentially attributable to weak indicators, redundancy, or inadequately modeled residual covariances. As a result, structural interpretations were put off until the model was improved.

• Respecifying the Model

After these results, the model was changed to make it simpler and clearer in terms of ideas. The improvement happened in two organized steps:

• Changes at the item level: Indicators that showed low or unstable factor loadings were taken out. In particular, TM4 (time management), CS4 (coworker support), and PS4 (perceived stress) were not included since their loadings were low in the EFA and first CFA. This stage adhered to psychometric best practices, which advocate for the elimination of items demonstrating cross-loading tendencies or inadequate communality (Marsh, 1996; Byrne, 2016) [40,45].
• Model simplification and correlated residuals: The measurement structure was simplified by keeping only the most representative indicators for each construct (two to three for each latent factor). Limited within-construct residual covariances were liberated due to theoretical and linguistic similarities among items; for instance, semantically overlapping statements within the same scale were utilized to accommodate modest method variance (Podsakoff et al., 2003) [39]. No cross-construct residual correlations were established, ensuring rigorous discriminant validity.

The final simple model kept the following indicators:

• Mindfulness (MN1, MN2);
• Time management (TM2, TM3);
• Control of the schedule (SC1, SC2, SC4);
• Perceived stress (PS1, PS2);
• Coworker support (CS1, CS2, CS3);
• Job performance (JP3, JP4);
• Work–life balance (WLB1, WLB2, WLB3).

For scale identification, one loading per construct was set to 1.00. All retained loadings were robust and statistically significant (p < 0.001), validating the integrity of the measurement quality.

• Last Measurement Model

After these changes, the model fit got a lot better and was almost perfect with JOB’s psychometric standards (see

Table 6. Model fit indices for the final CFA model.

Fit Index	Recommended Threshold	Obtained Value	Interpretation
χ2 (Chi-square)	Non-significant (sample-size-sensitive)	131.866, df = 100, p = 0.018	Significant (common with N ≈ 100)
χ2/df	≤3.00, acceptable	1.319	Acceptable
CFI	≥0.95, good (≥0.90, adequate)	0.972	Good fit
TLI	≥0.95, good (≥0.90, adequate)	0.962	Good fit
IFI	≥0.95, good (≥0.90, adequate)	0.973	Good fit
NFI	≥0.90	0.896	Borderline/adequate
GFI	≥0.90 (size-sensitive)	0.883	Slightly below
RMSEA (90% CI)	≤0.06, good (≤0.08, acceptable)	0.056 (0.024–0.080)	Good/near-close fit
PCLOSE	>0.05 supports close fit	0.348	Supports near-close fit
RMR	≤0.08, acceptable	0.054	Acceptable
PNFI	Higher is better (parsimony)	0.658	Parsimonious
PCFI	Higher is better (parsimony)	0.714	Parsimonious
AIC	Lower (comparative)	237.866	For model comparison
ECVI	Lower (comparative)	2.309	For model comparison

). The updated model produced χ²(100) = 131.87, p = 0.018; χ²/df = 1.319; CFI = 0.972; TLI = 0.962; IFI = 0.973; GFI = 0.883; and RMSEA = 0.056 (90% CI [0.024, 0.080], PCLOSE = 0.348).

The GFI value (0.883) was somewhat below the 0.90 benchmark, but this was seen as acceptable because the index is known to be sensitive to sample size (Byrne, 2016; Kline, 2016) [37,46]. Other indices, such RMR = 0.054, PNFI = 0.658, and PCFI = 0.714, also showed that the model was simple and good enough. As a whole, these measures show that the final measurement model had both theoretical coherence and empirical fit that were good for the structural analysis that came after it.

Importantly, the following constructs were examined: mindfulness (MN1, MN2), time management (TM2, TM3), schedule control (SC1, SC2, SC4), perceived stress (PS1, PS2), coworker support (CS1, CS2, CS3), job performance (JP3, JP4), and work–life balance (WLB1, WLB2, WLB3). Within-construct residual covariances were permitted when theoretically justified, and no cross-construct correlations were specified (see Figure 3).

Structural Effects

On the structural level, the improved model created a theoretically sound pattern that fit with both the Transactional and JDs-Rs frameworks. Time management became the most significant factor, strongly predicting the following:

• Control of the schedule (β = 0.885, p < 0.001);
• Coworker support (β = 0.556, p < 0.001);
• Job performance (β = 0.239, p = 0.041).

Job performance also had a highly positive link to work–life balance (β = 0.631, p < 0.001), showing that performance is an important way in which skill-based resources can improve well-being.

On the other hand, mindfulness → perceived stress (H1) and coworker support → job performance (H4) were not statistically significant (β = 0.065, p = 0.458). Mindfulness and social support did not show immediate effects. Their influence seemed indirect. Earlier studies suggest that mindfulness needs time before changes in stress can be measured (Khoury et al., 2015; Malinowski and Lim, 2015) [17,47].

Once the model was revised, the link between stress and work–life balance was no longer present. This points to a modeling issue rather than a real theoretical relationship.

Summary and Assessment

The constructs showed clear factor structures, acceptable internal consistency, and sufficient separation from one another. The model was refined by reducing the number of items, removing indicators that performed weakly, and keeping correlations between constructs within reasonable limits. Together, these steps strengthened both the theoretical clarity and the empirical soundness of the measurement model.

This measurement structure provides a solid foundation for the next stage of the analysis, which focuses on examining the proposed relationships between stress-management interventions, employee performance, and work–life balance. Thus, the successful CFA ensures that any structural inferences made are founded on psychometrically sound constructs, in accordance with JOB’s guidelines for empirical research in organizational behavior.

4.2. Qualitative Results

The qualitative part of the study offered more detail on how employees lived in and received perceptions of the stress-management interventions at Tayma University College. The participants’ perspectives on mindfulness lessons, time-management training, scheduling control activities, coworker support, and the association of these activities with stress, job performance, workplace relationships, and work–life balance were presented in the interview data. Both in the quantitative findings and in relation to other data, these accounts were useful for clarifying how psychological, structural, and social resources were experienced in a local work setting. Interview transcripts were coded, and analysis was conducted using NVivo 15 Plus according to the process described in Braun and Clarke (2006) [31]: six-phase thematic analysis. Combining coding informed by theory, the analysis applied to seven quantitative constructs was complemented by the data with other emerging themes. This method enabled the analytical effort to include both static dimensions and context-specific experiences. To underpin the analytic transparency of the analysis, the set of qualitative analysis visual outputs includes selective visual outputs to represent theme ordering and distributions. Figure 4 illustrates the relative frequency of coded references across themes, revealing that time management and schedule control are more common themes than mindfulness-linked factors. The general thematic structure is shown in Figure 5, while Figure 6 describes relationships between stress-management interventions and employee outcomes. These figures are intended to complement the interpretation provided in this section, not replace it. The qualitative codebook of code definitions and examples are given in Supplementary Materials, where coding categories were developed from the seven quantitative constructs and additional themes were added to capture variations and nuances found across participants’ accounts.

4.2.1. Mindfulness and Stress Perception (H1)

Another key aspect that emerged from the qualitative phase was the scarce reference to mindfulness as an intentional tool to manage stress. Participants rarely incorporated mindfulness-based practices (e.g., focused breathing, awareness exercises, cognitive reframing) into their standard coping strategies. It seems that mindfulness was not particularly woven into employees’ daily work tasks.

Indeed, as indicated by Figure 4, mindfulness-related codes were less commonly identified than codes connected to other interventions, reinforcing the limited implementation of mindfulness in daily work conditions.

This qualitative pattern is consistent with the quantitative findings in that they did not support the expected negative association (β = 0.065, p = 0.458) between mindfulness and perceived stress. Mentions of mindfulness were not associated with lower reported levels of stress in this sample, at least not in a practical sense. Based on the Transactional Model of Stress and Coping (Lazarus and Folkman, 1984) [5], outcomes of stress depend not only on exposure to stressors but also on appraisal and coping. This lack of investment in mindfulness practices in the interviews may thus have inhibited the cognitive reappraisal skills needed for lessening the stress response. This result may also capture a “psychological paradox” observed in mindfulness research, characterized by the idea that a person’s self-knowledge can at some point in time amplify their perceived stress, before coping increases in the future (Malinowski and Lim, 2015; Donald et al., 2019) [47,48]. Further, there may have been other contextual reasons, such as general attitudes towards more introspective approaches, conflicting work obligations, and weak institutional support, which limit the implementation of mindfulness in the workplace. Overall, the qualitative findings were in agreement with the quantitative results in predicting that despite its strength, there are indications of limited implementation of mindfulness in practice. This emphasizes the necessity for cultural transition and sustained organizational reinforcement for the successful embedding of mindfulness-based interventions in Saudi HEIs (Khoury et al., 2015; Ong et al., 2024; Dou et al., 2025) [17,49,50].

4.2.2. Time Management and Job Performance (H2)

Time management was highlighted as a practice that participants had made an effort to practice (or that at least was actively included) in their daily work patterns, as is evident in the qualitative data. Training was said to have made a tangible difference in the way they managed their tasks and duties. Frequently, participants cited the concept of clearer prioritization, reducing delays in task completion, more realistic scheduling, and greater consistency in meeting deadlines as hallmarks.

As shown in Figure 4, time management seemed to be the most common intervention type, showing time management as the most important factor that influences everyday work habits. It helped maintain balance between academic, administrative, and personal needs during work, participants said. Some also felt more in control and less distracted by “unfinished tasks” when they worked within predetermined timetables, which facilitated extended periods of commitment and output.

These qualitative reports align well with the quantitative findings, which found that time management was a significant predictor of job performance (β = 0.239, p = 0.041). The results support self-regulation perspectives that include advanced goal setting and progress monitoring to sustain task focus to mitigate cognitive overload (Aeon et al., 2021; Bedi and Sass, 2023) [21,51].

The findings indicate that time management is a skill-based personal resource by which people can manage their high demands by increasing perceived control and decreasing the level of stress from a Job Demands–Resources (JDs–Rs) point of view. Between these two views, all of the qualitative and quantitative evidence suggests that time management was the most practically embedded component of the intervention strategy.

4.2.3. Time Management and Coworker Support (H3)

The interview results showed that time management increased beyond individual efficiency with positive influence on social interactions at work. Getting there on time, doing things the same every time, and being able to predict when items would get done, participants said, helped members of staff to trust one another more and work like they ought to work together better. Respondents said that sticking to timetables made collaborating and going along easier and helped other people support each other by relieving last-minute stress. The interrelated themes depicted in Figure 5 illustrate the way in which time-management practices indirectly fostered coworker collaboration and support.

Coworker support was discussed extensively during the interviews. Many participants described supportive colleagues as an important reason why they were able to continue performing their work, especially during demanding periods. Support did not always take formal forms. In some cases, it involved listening or offering reassurance. In other cases, it involved sharing experience, giving advice, or helping to resolve small problems before they escalated.

From this point of view, social exchange theory helps explain these developments, as it suggests that trust and cooperation build through repeated experiences of reliability and mutual support. Both sources of data in this study pointed in a similar direction. Findings from the qualitative interviews were largely consistent with the results of the quantitative SEM analysis. Time management was strongly and significantly associated with coworker support (β = 0.556, p < 0.001).

The interview data made this relationship easier to understand. Participants did not describe time management as a technical issue on its own. Instead, they spoke about it in relation to working with others. When deadlines were clear and schedules were followed, coordination tended to improve. Roles were easier to understand, and everyday work involved fewer sources of tension. Similar experiences were shared by many participants across interviews. Similar patterns have been reported in earlier research. Previous studies suggest that reliable time-related behavior helps strengthen collaboration and reduces conflict among coworkers (Kelly, 2002; Peeters and Rutte, 2005) [22,23].

In the organizational context examined here, time management therefore appeared to play a role that went beyond efficiency alone. Over time, these everyday practices supported more stable and trusting relationships between colleagues. It can be understood as a form of relational capital that adds value to both performance and the long-term sustainability of the workplace.

4.2.4. Coworker Support and Job Performance (H4)

Coworker support emerged as a recurring theme in the interviews. Many participants described supportive colleagues as one of the reasons they were able to continue with their work, particularly during more demanding periods. What they referred to was not limited to formal support arrangements. In some situations, support took the form of simply being available to listen or to offer reassurance. In others, it emerged through the sharing of experience, practical advice, or timely help with small matters before they turned into larger concerns. While these exchanges were part of ordinary day-to-day work, participants repeatedly stressed that they made a clear and tangible difference.

Seen in this light, these experiences resonate with both the Job Demands–Resources framework and social capital theory, both of which draw attention to the role of workplace relationships, especially in contexts where demands are high. Through ongoing interaction and mutual support, such relationships can help employees manage pressure and sustain effective performance. Through emotional support and opportunities for shared learning, social ties can help employees manage pressure and sustain their capacity to perform. The quantitative findings, however, presented a somewhat different picture. The analysis did not reveal a statistically significant relationship between coworker support and job performance (β = 0.065, p = 0.458). At first, this difference did not sit easily with what participants described in the interviews. Looking more closely, it seems more likely that the issue lies in how coworker support was measured rather than in the absence of a real relationship. In particular, the CFA results showed that one of the items used to capture coworker support (CS4) performed weakly, which may have made the construct less clearly represented in the model.

Existing research has repeatedly shown that support from coworkers is linked to better job performance, especially in work environments that depend on coordination and close interdependence, such as universities (Chiaburu and Harrison, 2008; Yousef, 2024) [2,24]. Seen against this background, the qualitative findings from this study suggest that coworker support remains an important part of everyday work, even when its effects are not fully reflected in statistical analyses. From an organizational sustainability perspective, supportive relationships help reduce strain, support well-being, and make it easier for employees to maintain performance over time.

Thus, while coworker support did not emerge as a direct predictor of job performance in the SEM model, the qualitative evidence suggests an indirect and buffering role that may not be fully captured by performance-based indicators.

As reflected in Figure 4, coworker-support-related codes were less frequently associated with direct performance outcomes while appearing more prominently in narratives related to emotional resilience and stress buffering, helping to explain the non-significant quantitative relationship.

4.2.5. Time Management and Schedule Control (H5)

Throughout the interviews, participants consistently associated proficient time management with enhanced schedule control. Employees said that learning to plan ahead helped them predict when they would have a lot of work to perform, be more flexible with their time, and coordinate work activities with personal obligations.

As reflected in the thematic hierarchy shown in Figure 6, schedule control functioned as a downstream outcome of disciplined time-management practices.

Interview participants frequently referred to greater predictability in their daily schedules and an improved ability to complete tasks within regular working hours. What participants described pointed to a growing sense of autonomy in how they organized their work time. This feeling of having control over one’s schedule was echoed in the quantitative results, which showed a strong link between time management and schedule control in the SEM analysis (β = 0.885, p < 0.001).

Seen this way, the findings resonate with core ideas in the Job Demands–Resources framework, where personal resources are understood as something that can gradually shape more structural aspects of the job (Hobfoll, 2011) [52]. Earlier studies have reached similar conclusions, showing that being able to structure one’s time effectively is often linked to greater autonomy and a lower sense of overload (Hafner et al., 2010; Aeon and Aguinis, 2017) [25,26]. Recent research reveals that having some control over one’s schedule is important in shaping how everyday time management affects work–life balance (Mansour and Abu Tayeh, 2024) [3].

Looking at the qualitative and quantitative findings together, schedule control in this setting appears to have grown out of employees’ own ways of managing their time in practice, rather than being imposed solely through formal organizational arrangements, which in turn helped them deal more effectively with overlapping and competing work demands.

4.2.6. Job Performance and Work–Life Balance (H6)

Many participants said that working well helped them keep work from getting in the way of their personal lives. When tasks were performed well, work did not feel as heavy and it was easier to put off until the next day. A few people said that once they stopped thinking about unfinished work, they could be more present with their families or find time for themselves. For some people, finishing their work gave them a sense of peace that stayed with them after work, making them feel calmer and more settled after a busy day. This kind of carryover reflects what enrichment theory describes as the transfer of resources from work to other areas of life (Greenhaus and Powell, 2006) [27]. The survey results point in the same direction, showing a strong link between job performance and work–life balance (β = 0.631, p < 0.001).

Seen together, these findings suggest that job performance is not only something organizations benefit from. For employees themselves, performing well can act as a resource that eases tension between roles and supports recovery after demanding workdays. Earlier research has noted similar patterns, showing that higher performance is often linked with less interference between work and home and with better overall well-being (Demerouti et al., 2004; Haar et al., 2014) [53,54].

From this perspective, job performance appears to play a quiet but important role in helping employees move from work to home more smoothly, reducing strain and supporting a healthier balance between the two.

4.2.7. Time Management, Job Performance, and Work–Life Balance (H7)

Participants talked about time management as something that helped everything else fall into place. When people managed their time more effectively, they felt more in control of their day, finished what they needed to do, and could leave work behind more easily once the day ended. Several participants said this had a noticeable effect on their everyday lives. Finishing tasks on time eased pressure during the workday and made it easier to turn attention to life outside work (Macan, 1994; Claessens et al., 2007; Aeon et al., 2021) [11,21,28].

Many described this as something that gradually reinforced itself. When daily tasks felt under control, their work performance improved. When they felt they were doing their jobs well, it became easier to protect time for family and personal activities. Work stopped spilling over into the rest of the day (Greenhaus and Powell, 2006) [27].

Figure 6 reflects this pattern by showing how time management connects to job performance and how performance then relates to work–life balance. The interview accounts follow the same pattern seen in the survey results, where time management was linked to work–life balance through its effect on performance (indirect β = 0.151, p < 0.05).

Taken as a whole, this suggests that time management mattered for work–life balance mainly because it shaped how people experienced their work on a day-to-day basis.

A Summary of Qualitative Insights

What participants described in the interviews suggested that they did not see the stress-management interventions as separate efforts. Instead, these elements were experienced as part of everyday work rather than as separate practices. Mindfulness was mentioned less often, but time management was mentioned more often as being more important. Participants explained that these practices helped them work more smoothly with others and manage their time more effectively. When these habits came together, they were linked to better performance at work and to feeling better overall. Seen this way, personal abilities such as time management appear to create space for other kinds of support to develop at work. This highlights the value of approaching stress management in a way that reflects the local context—by strengthening individual skills while also supporting everyday collaboration and organizational practices within Saudi universities.

Taken together, the convergence of thematic insights, coding patterns, and conceptual links supports the coherence of the qualitative results and their alignment with the quantitative evidence.

5. Discussion

This study examined the effect of different stress-management interventions (mindfulness sessions, time-management workshops, schedule control, and support from colleagues) on employee well-being and staff performance at Tayma University College. Based on a convergent mixed-methods design, it joined quantitative structural modeling with qualitative thematic analysis to include both a structural model and qualitative analysis as an investigation of measured and lived dimensions of employee adaptability to work-related stress and resilience. The results provide input in a growing discourse regarding the utility of resource-based and coping-based paradigms (e.g., the Transactional Model of Stress and Coping (Lazarus and Folkman, 1984) [5] and the Job Demands–Resources (JDs–Rs) model (Bakker and Demerouti, 2007) [6]) in higher education institutions in non-Western, rapidly changing contexts such as Saudi Arabia. The findings showed considerable agreement with theoretical expectations in the field of time management and job performance, while substantial discrepancies were noted, such as the low role of mindfulness and the variable statistical significance of coworker support. The discussion below places these findings in a broader theoretical and cultural context.

5.1. Mindfulness and Stress Reduction (H1)

The proposed negative correlation between mindfulness and perceived stress lacked statistical validation, a result corroborated by qualitative data, which indicated that employees seldom identified mindfulness as a prominent coping strategy. This result differs from what has commonly been reported in international studies, where mindfulness is often linked to lower stress and emotional exhaustion (Khoury et al., 2015; Vonderlin et al., 2020) [17,18]. One possible reason for this difference is that the context and the length of time spent on it matter. When mindfulness programs are brief or removed from their everyday context, they may first make people more aware of what stresses them, before they learn how to deal with it. Malinowski and Lim (2015) [47] refer to this early stage as the awareness paradox. At this stage, stress may feel more noticeable rather than reduced. Over time, and with more regular practice, this tends to ease. In this setting, there was little ongoing support to help participants keep practicing. Without follow-up sessions, mindfulness was harder to sustain as a habit, and its effects were therefore less visible. Cultural background played an important role, as participants often relied on prayer, dhikr, and their faith as their main ways of coping with stress. From this perspective, Western-style mindfulness practices may not have felt natural or closely aligned with their everyday coping approaches. Research suggests that effectiveness alone is not enough for an intervention to be adopted. When practices feel culturally distant, people may be reluctant to use them, even if they have shown benefits in other contexts (Hall et al., 2016; Alsubaie et al., 2017) [55,56]. This points to issues of fit, not to a problem with the theoretical foundations of mindfulness. Rather, it points to the importance of how mindfulness is adapted to its context. Previous studies suggest that interventions are more effective when they fit with local religious and social values (Koenig, 2012; Padela and Curlin, 2013) [46,57]. In this context, mindfulness may work better when it connects with familiar practices. Pairing breathing exercises with dhikr could help it feel more natural. Seen this way, the limited results appear to be linked to both implementation and cultural fit. These results suggest that, in Saudi higher education, mindfulness-based approaches may need to be more closely rooted in local practices and experiences if they are to support stress management in a meaningful way.

5.2. Time Management as a Primary Factor (H2, H3, H5, H7)

Time management emerged as the most powerful, multi-faceted enabler in quantitative and qualitative measurements. Statistically, time management was a strong predictor of job performance (H2), colleague support (H3), and schedule control (H5). It even indirectly supported work–life balance via job performance (H7). Employees shared how the systematic planning process, prioritization, and controlled scheduling improved productivity, reduced procrastination, and helped develop trust between coworkers. The results are in line with previous studies that associate time-management practices with higher productivity and quality of life (Aeon et al., 2021; Bedi and Sass, 2023) [21,51]. In the JDs–Rs model, time management shows how a person’s personal resources can turn into structural resources (like schedule control) and social resources (such as coworker support). This initiates a gain spiral, which increases motivation and resilience (Hobfoll, 2011) [52]. The significant relationship between time management and schedule control (β = 0.885, p < 0.001) bolsters Hafner et al. (2010) and Aeon and Aguinis (2017) [25,26], who showed that proactive time structuring promotes perceived autonomy and mitigates overload. Similarly, the indirect impact of time management on work–life balance through job performance mirrors the work–family enrichment pathway proposed by Greenhaus and Powell (2006) [27] and that resources developed in the workplace (skills, confidence, and good affect) are beneficial to other areas of life. Taken together, these results emphasize that time management plays a role not as a “behavioral” tool but a “developmental” resource. This allows employees to manage cognitive, temporal, and relational demands simultaneously, as well as showing theoretical compatibility and considerable practical relevance in the Saudi university context.

5.3. Coworker Support and Job Performance (H4)

The qualitative evidence strongly emphasized the perceived positive role of coworker support in job performance, despite the absence of a statistically significant association in the quantitative analysis. Employees believed that their relationships with their peers were critical for alleviating stress, sharing information informally, and maintaining morale during challenging periods. This difference is probably due to measurement limitations, as opposed to a real theoretical conflict. In the CFA, one item from the coworker support scale (CS4) experienced relatively weak loading, consistent with prior research that suggests that many of the Job Content Questionnaire items do not provide consistent results across context changes (Karasek et al., 1998; Wong et al., 2003) [7,41]. Moreover, Chiaburu and Harrison (2008) and Yousef (2024) [2,24] observed that performance is influenced by coworker support contingent on task dependency and collective identity. This is because university work involves considerable individual duties (such as teaching, researching), and the impact of peer support is likely to be more diffuse. Thus, the non-significant pathway in SEM must not be read as a theoretical retort but as a consequence of measurement and context. Qualitative investigations support the idea that coworker assistance constitutes a critical social resource, particularly in academic contexts characterized by bureaucratic change and an uneven workload distribution. Further research could employ culturally specific measures or multilevel models to better characterize the contextualized effect of peer relations.

5.4. Job Performance and Work–Life Balance (H6)

The results showed a strong positive relationship between job performance and work–life balance (β = 0.631, p < 0.001), a pattern that appeared in both the quantitative and qualitative data. Employees explained that performing their work well often reduced everyday pressures. Tasks felt easier to handle, urgent demands became less overwhelming, and the boundaries between work and personal life were clearer, both in the workplace and at home. In this way, higher productivity was closely linked to a stronger sense of balance between work and life.

These findings are in line with the idea that success in one area can generate resources that carry over into other areas, such as greater energy, more available time, and positive emotions (Greenhaus and Powell, 2006) [27]. From a JDs–Rs perspective, job performance can therefore be seen as a personal resource. Performing well may strengthen self-efficacy and provide emotional rewards that help individuals manage competing demands more effectively.

Similar observations can also be found in earlier studies, where strong job performance was linked to how employees deal with work–home demands and recovery over time (Demerouti et al., 2004; Haar et al., 2014) [53,54].

5.5. Model Refinement and Exclusion of Non-Significant Paths

To create a simple and reliable structural model, a number of weak indicators and non-significant routes were carefully left out. The pathway from mindfulness to perceived stress (H1) was eliminated due to its lack of importance, aligning with SEM best practices that prioritize clarity and model parsimony (Byrne, 2016; Kline, 2016) [30,45]. The link between coworker support and job performance (H4) was not retained in the final model. This decision draws on earlier work suggesting that social support tends to influence performance indirectly, often by affecting factors such as engagement or emotional exhaustion, rather than having a direct effect on performance outcomes (Podsakoff et al., 2003) [39].

At the measurement stage, three items (TM4, PS4, and CS4) were removed because they did not perform well statistically. Previous psychometric research has noted that items with reverse wording or ambiguous phrasing can introduce unwanted method effects and blur the structure of latent constructs (Marsh, 1996; Netemeyer, Bearden, and Sharma, 2003) [40,42]. Removing these items resulted in a clearer measurement model and noticeably improved overall fit, with CFI and TLI values rising above 0.95. At the same time, this refinement improved factorial validity while leaving the theoretical basis of the model intact. From this perspective, the way the model was refined shows an effort to make the results clearer without losing sight of the theoretical ideas on which the analysis was built (Hair et al., 2019) [44].

5.6. Synthesis and Theoretical Contributions

When seen together, the findings demonstrate a group of interrelated resources rather than isolated effects. Time management emerged as a particularly important skill, setting other processes in motion. It supported greater control over work schedules, facilitated smoother interaction with colleagues, and was associated with improvements in job performance and work–life balance. In this sense, its influence unfolded gradually, spreading across different areas of work rather than operating in a single, direct way. This pattern is consistent with the motivational logic of the Job Demands–Resources framework and appears to be especially relevant in the context of Saudi higher education.

Job performance played a dual role within this process. It reflected the successful use of available resources while also acting as a resource in its own right by supporting well-being and positive spillover beyond work. This dynamic is in line with enrichment theory, which emphasizes how experiences and gains in one role can strengthen functioning in other areas of life (Greenhaus and Powell, 2006) [27]. By contrast, mindfulness and coworker support showed more limited or context-dependent effects. These weaker patterns may reflect cultural expectations as well as challenges related to how these constructs were measured, rather than a lack of relevance altogether. In this way, the findings underline the importance of considering context when applying stress and coping models across different cultural settings.

Rather than simply replicating earlier studies in a new location, this study highlights how established theories operate under specific boundary conditions. The results suggest that stress-management interventions do not function in the same way everywhere but are shaped by local beliefs, norms, and institutional arrangements within non-Western organizational contexts.

From a practical perspective, these insights carry clear implications for Saudi universities, particularly in light of the reforms associated with Vision 2030. Interventions that focus on concrete, skill-based approaches—such as time-management training and greater flexibility in scheduling—appear especially promising. At the same time, initiatives related to mindfulness and peer support may require adaptation so that they better fit local expectations and everyday work realities.

The findings can be better understood when viewed through cultural perspectives relevant to the Saudi context. Work on cultural dimensions has shown that Saudi society tends to be shaped by relatively high levels of collectivism and power distance, which influence how authority, social relationships, and self-regulation are perceived [15]. Within such a context, practices that are visible, structured, and embedded in everyday interactions—such as time management—are likely to be more readily accepted and more consistently practiced than approaches that rely primarily on individual introspection.

Islamic Work Ethics (Ali, 1988) [16] provide an additional lens through which these patterns can be understood. Core values such as diligence (itqan), responsibility (amanah), respect for time, and fulfilling obligations toward both work and family occupy a central place within this ethical tradition. Time management fits naturally with these values, which may help explain its strong and consistent influence. By contrast, mindfulness practices that draw on Western contemplative traditions may feel less familiar unless they are clearly connected to locally grounded practices, such as dhikr or forms of reflection rooted in Islamic teachings.

Together, these cultural and ethical perspectives help clarify why certain resources carried more weight than others. They point to the importance of alignment between stress-management interventions and the social and value systems within which they are introduced. In this sense, the study contributes to the Job Demands–Resources framework by showing that the effectiveness of resources is not universal but shaped by their fit with the cultural and organizational context in which they are applied.

6. Conclusions

This study explored stress-management practices at Tayma University College, focusing on how employees actually experienced them in their daily work. The programs included mindfulness activities, time-management sessions, flexible scheduling, and peer support. Rather than treating these as isolated initiatives, the study combined survey analysis with interviews to understand what seemed to work, what did not, and why.

The results showed clear differences across the interventions. Time management and schedule flexibility appeared to matter more consistently, while mindfulness and peer support played a role mainly in certain situations. These patterns offer a practical view of how employee well-being can be supported in higher education settings, especially when attention is paid to everyday work realities.

6.1. Theoretical Contributions

This study draws on the Saudi higher education context to examine how personal and organizational resources come together in shaping employees’ experiences of work. The findings point to time management as a particularly important personal capability. In practice, it supported greater control over work schedules and smoother interaction with colleagues, setting in motion positive processes that were reflected in both job performance and work–life balance.

By contrast, mindfulness showed a more limited statistical role. This suggests that widely used stress-management approaches may not function in the same way across settings and that their application often requires adjustment to local cultural, religious, and institutional conditions. Seen this way, existing models of coping and resources need to be applied with closer attention to context. The findings suggest that cultural differences play a role in shaping how stress-related resources operate in non-Western workplaces.

6.2. Contributions to Practice

The findings point to several practical implications for university administrators and decision-makers. Time-management training and greater control over work schedules emerged as particularly effective ways to support both productivity and employee well-being. Universities could benefit from offering structured activities that focus on planning, setting priorities, and managing workloads while also putting scheduling arrangements in place that make room for balance between work and personal life.

Mindfulness and coworker support also appear relevant, but their effectiveness seems to depend on how they are designed and introduced. In the Saudi context, mindfulness practices may be more meaningful when they are linked to familiar traditions, such as dhikr (remembrance) or other forms of reflective practice grounded in Islamic values. In a similar way, peer support may have greater impact when informal interactions are supported through more organized mentoring or collaborative arrangements that fit the institutional setting.

These directions are in line with the broader aims of Saudi Arabia’s Vision 2030, which places strong emphasis on human development and employee well-being as part of long-term institutional progress.

6.3. Limitations

Several limitations of the study should be noted. To begin with, the cross-sectional design means that conclusions about cause and effect must be drawn with caution. Designs that follow participants over time, or that involve experimental elements, would be better suited to capturing how relationships develop and change. In addition, while the sample size (N = 104) was adequate for the analyses conducted, it places some limits on how far the findings can be generalized beyond the study setting.

Cultural context also appears to have played a role, particularly in how certain interventions, such as mindfulness, were received and used in practice. This makes it difficult to assume that similar patterns would emerge in other settings. Finally, some measurement challenges were encountered. A small number of items, especially those that were reverse-coded (e.g., TM4, PS4, CS4), did not perform as expected and were therefore removed, which may have narrowed the coverage of some constructs. Even so, the use of both survey data and interviews allowed the findings to be viewed from more than one angle, strengthening the overall interpretation of the results.

6.4. Directions for Future Research

Building on these findings, several directions appear worth pursuing in future research. One area concerns mindfulness, where greater attention could be given to approaches that are adapted to local cultural and religious traditions, in order to improve both acceptance and practical relevance. Another step would be to broaden the scope of investigation by including a wider range of universities and organizational settings within Saudi Arabia, which would help clarify how far the findings extend beyond a single context.

Future studies could also benefit from designs that follow participants over time or examine specific interventions in practice. Such approaches would make it possible to better understand whether the effects of resource-based interventions are sustained and how they unfold. In addition, there is a clear need for more carefully tailored measurement tools for coworker support, particularly ones that reflect non-Western cultural and linguistic settings more accurately.

Together, these lines of inquiry would deepen theoretical understanding while also offering practical guidance for developing well-being initiatives that are both culturally grounded and informed by evidence.

6.5. Overall Consequences

In closing, the study shows that stress-management approaches do not all work in the same way or to the same degree. Practices related to time management and control over work schedules stood out as consistently helpful for supporting both job performance and employee well-being. Other approaches, such as mindfulness and coworker support, appeared to be more sensitive to context and may require further cultural and methodological adjustment before their potential can be fully realized in local work settings.

By bringing together quantitative analysis and qualitative accounts, the study provides a grounded picture of how employees navigate work demands and available resources in their daily lives. The findings contribute to broader discussions of occupational stress while also offering practical guidance for Saudi higher education institutions. In particular, they point to the value of developing strategies that fit local cultural conditions and support both organizational goals and employee well-being, in line with the broader aims of Vision 2030.